code: def __process_data(
      data_folder: str,
      dst_folder: str,
      manifest_file: str,
      num_workers: int,
  ):
      """
      Converts flac to wav and build manifests's json
      Args:
          data_folder: source with flac files
          dst_folder: where wav files will be stored
          manifest_file: where to store manifest
          num_workers: number of parallel workers processing files
      Returns:
      """
  
      if not os.path.exists(dst_folder):
          os.makedirs(dst_folder)
  
      files = []
      entries = []
  
      for root, dirnames, filenames in os.walk(data_folder):
          for filename in fnmatch.filter(filenames, "*.trans.txt"):
              files.append(os.path.join(root, filename))
  
      with multiprocessing.Pool(num_workers) as p:
          processing_func = functools.partial(
              __process_transcript, dst_folder=dst_folder
          )
          results = p.imap(processing_func, files)
          for result in tqdm(results, total=len(files)):
              entries.extend(result)
  
      with open(manifest_file, "w") as fout:
          for m in entries:
              fout.write(json.dumps(m) + "\n")
  
file_path: NeMo/scripts/dataset_processing/get_librispeech_data.py
imports: import argparse
  import fnmatch
  import functools
  import json
  import logging
  import multiprocessing
  import os
  import subprocess
  import tarfile
  import urllib.request
  from sox import Transformer
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling a global function with example usage: __process_data(data_folder, dst_folder, manifest_file, num_workers) without return types